Arctic dust from above and below: estimating the unseen contributions of northern high latitude dust sources

Emissions of mineral dust into the atmosphere from land surfaces susceptible to wind erosion are highly significant for understanding the Earth's environmental system as a whole. Appreciation of the wide-ranging environmental influence that dust exerts is such that a full "dust cycle" concept - involving dust's uplift, through its atmospheric residence time, to eventual deposition - is now recognised. Recently, sources of dust located in the high latitudes (greater or equal to 50 degrees N and greater or equal to 40 degrees S), particularly the Arctic, have emerged as a potentially significant contributor of dust in the global system, especially due to large volumes of sediment supply from the glacial-fluvial system.
Broad-scale patterns of dust emissions are reasonably well-constrained, in terms of the global distribution of dust sources and the seasonality of dust emissions. There is, however, recognition that certain data sources may cause dust emissions to be under-estimated and that this under-estimation may be particularly biased towards particular types of dust events. For example, measurements of local, near-surface dust emissions can quantify at-a-point dust activity, but there are challenges with broader scaling of these measurements. Remote sensing offers a broader view, but significant dust events can go undetected by satellite imagery e.g., due to satellite overpass time or presence of cloud. When there is cloud cover, dust emissions can be substantially under-estimated by remote sensing approaches and this particular challenge needs exploring for high latitude dust source regions with persistent and extensive cloud.

This project will integrate ground-based, climate and satellite remote sensing datasets to determine how different data sources represent blowing dust events in the Arctic. The goal to understand the data limitations and hence how under- or over-estimation of dust may be accounted for. Understanding the biases and errors...